An integrated framework for ADN congestion management combining PLLM-based forecasting and goose optimization

Abstract

This paper proposes an innovative congestion management method for Active Distribution Networks (ADNs) by integrating a Pre-trained Large Language Model (PLLM) with the Goose Optimization (GO) algorithm to tackle challenges posed by the uncertainty of flexible resources. Initially, a short-term power forecasting method is developed using PLLM enhanced with Low-Rank Adaptation (LoRA). This approach precisely captures the dynamic fluctuations of power sources and loads, achieving R2 of 0.96 and 0.95 in wind and load forecasting, respectively. Compared to traditional algorithms (BP, LSTM, BiLSTM), it reduces MAE and RMSE by at least 39.08% and 19.68% for wind forecasting, and 15.82% and 21.80% for load forecasting, respectively, ensuring high-accuracy inputs for the subsequent optimization process. Subsequently, to address the bottlenecks of slow convergence and inefficiency inherent in conventional algorithms for large-scale scheduling, the novel GO algorithm is utilized as the core engine to solve the complex optimal power flow problem. Multi-scenario simulations on the IEEE 33-bus system validate that the proposed framework effectively alleviates line overloads and voltage violations, with the GO algorithm improving scheduling efficiency by at least 65.50% compared to the improved PSO. These findings highlight the exceptional performance and considerable potential of the combined PLLM and GO approach for improving the operational security, economic viability, and scheduling efficiency of ADNs.